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Gradient boosting machines, a tutorial

Natekin, Alexey ; Knoll, Alois

Frontiers in neurorobotics, 2013-01, Vol.7, p.21-21 [Periódico revisado por pares]

Switzerland: Frontiers Research Foundation

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  • Título:
    Gradient boosting machines, a tutorial
  • Autor: Natekin, Alexey ; Knoll, Alois
  • Assuntos: Algorithms ; Artificial intelligence ; Bioinformatics ; boosting ; Classification ; Design ; gradient boosting ; Learning algorithms ; Machine learning ; Methods ; Neural networks ; Neuroscience ; regression ; Researchers ; text classification
  • É parte de: Frontiers in neurorobotics, 2013-01, Vol.7, p.21-21
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
    Reviewed by: Olivier Michel, Cyberbotics Ltd., Switzerland; Frederic Alexandre, Universiy of Bordeaux, France
    Edited by: Marc-Oliver Gewaltig, Ecole Polytechnique Federale de Lausanne, Switzerland
    This article was submitted to the journal Frontiers in Neurorobotics.
  • Descrição: Gradient boosting machines are a family of powerful machine-learning techniques that have shown considerable success in a wide range of practical applications. They are highly customizable to the particular needs of the application, like being learned with respect to different loss functions. This article gives a tutorial introduction into the methodology of gradient boosting methods with a strong focus on machine learning aspects of modeling. A theoretical information is complemented with descriptive examples and illustrations which cover all the stages of the gradient boosting model design. Considerations on handling the model complexity are discussed. Three practical examples of gradient boosting applications are presented and comprehensively analyzed.
  • Editor: Switzerland: Frontiers Research Foundation
  • Idioma: Inglês
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